Postprocess.R

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#File description comment, including purpose of program, inputs
# and outputs

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sumSquares <- function(data, clusters){
  # Given the data and clusters vector this function computes 
  # the between and within sum squered errors
  #
  # Args:
  #      data: After Principal component data  
  #      clusters: The  vector of integers indicating the
  #                cluster to which each point is allocated.
  #
  # Returns:
  #      A list with two values SSW for Sum Squered Within
  #      and SSB for Sum Squered Between
  # 
  # Error handeling
  ####################################################
  cluster.number <- max(clusters)
  ssw <- rep(0,cluster.number)
  ssb <- rep(0,cluster.number)
  center <- apply(data,2,mean)
  for(i in 1:cluster.number){
    j <- clusters==i
    D <- data[j,]
    centerw <- apply(D,2,mean)
    for(k in 1: nrow(D)){
    ssw[i]<- ssw[i] + sum((D[k,]-centerw)^2) 
    }
    ssb[i] <- sum(j)*sum((centerw-center)^2)
  }
  out <- list(SSW=sum(ssw),SSB=sum(ssb))
  return(out)
}

mapping <- function( long,lat ,clusters){
  # Useing map database draw cluster on the US Map
  #
  # Args:
  #     long: A numeric vector of  longitude location of the points.
  #     lat: A numeric vector of latitude location of the points.
  #     clusters: The  vector of integers indicating the
  #               cluster to which each point is allocated.
  #
  #
  # Error handeling
  ####################################################
  map("state",xlim=c(-98,-65),ylim=c(35,50),col='gray90',fill=TRUE)
  cluster.number <- max(clusters)
  map.axes()
  for(i in 1:cluster.number){
    j <- clusters==i
    points(long[j],lat[j],pch=19,col=i,cex=0.25)
  }
}